annotated_deep_learning_paper_implementations/labml_nn/diffusion/ddpm/experiment.ipynb

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   "source": [
    "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)\n",
    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)\n",
    "\n",
    "## [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)\n",
    "\n",
    "This notebook trains a DDPM based model on MNIST digits dataset."
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "AahG_i2y5tY9",
    "pycharm": {
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   },
   "source": [
    "### Install the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "pycharm": {
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   "outputs": [],
   "source": [
    "!pip install labml-nn comet_ml --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Enable [Comet](https://www.comet.ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#@markdown Select in order to enable logging this experiment to [Comet](https://www.comet.ml).\n",
    "use_comet = False #@param {type:\"boolean\"}\n",
    "\n",
    "if use_comet:\n",
    "    import comet_ml\n",
    "    comet_ml.init(project_name='diffusion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SE2VUQ6L5zxI",
    "pycharm": {
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   },
   "source": [
    "### Imports"
   ]
  },
  {
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    "collapsed": false,
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     "outputs_hidden": false
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   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from labml import experiment\n",
    "from labml.configs import option\n",
    "from labml_nn.diffusion.ddpm.experiment import Configs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Create an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "experiment.create(name=\"diffuse\", writers={\"screen\", \"comet\"} if use_comet else {'screen'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
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   },
   "source": [
    "### Configurations"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "collapsed": false,
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   "outputs": [],
   "source": [
    "configs = Configs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Set experiment configurations and assign a configurations dictionary to override configurations"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "experiment.configs(configs, {\n",
    "    'dataset': 'MNIST',\n",
    "    'image_channels': 1,\n",
    "    'epochs': 5,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Initializ"
   ]
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   "metadata": {
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   "outputs": [],
   "source": [
    "configs.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EvI7MtgJ61w5",
    "pycharm": {
     "name": "#%% md\n"
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   },
   "source": [
    "Set PyTorch models for loading and saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "GDlt7dp-5ALt",
    "outputId": "e7548e8f-c541-4618-dc5a-1597cae42003",
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   "outputs": [],
   "source": [
    "experiment.add_pytorch_models({'eps_model': configs.eps_model})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Start the experiment and run the training loop."
   ]
  },
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   },
   "outputs": [],
   "source": [
    "# Start the experiment\n",
    "with experiment.start():\n",
    "    configs.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
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